gobrave - Bioinformatics Analysis Platform

One Engine. Three Frictions Removed.

Most bioinformatics teams fight the same three frictions:

  1. Workflow logic lives in one place, runtime behavior in another.
  2. Data lineage is reconstructed after the run, not during the run.
  3. Reproducibility depends on tribal knowledge.

gobrave is built to remove these frictions at the platform level. It is a Go-native execution system where workflow, runtime, and data traceability are designed as one model.

Product Philosophy

Reproducibility Is A Runtime Property

In gobrave, reproducibility is not a report artifact. It is encoded into execution boundaries, status transitions, and replay-safe orchestration.

Throughput Without Operational Chaos

Container creation queues, lifecycle transitions, retries, and cleanup policies are part of core behavior, not bolted-on scripts.

Deterministic First, Adaptive When Needed

Start with static DAG execution. Evolve to dynamic node materialization and reactive streaming without migrating to another platform.

Capability Map

Workflow Orchestration

  • DAG compiler and scheduler
  • Scatter/gather fan-out and merge patterns
  • Incremental reruns with cache-aware behavior

Runtime Control

  • Docker, k8s, and k3s runtime abstraction
  • Async lifecycle worker with durable outbox events
  • Runtime monitor with restart-safe reconciliation

Data System

  • Project-scoped dataset, sample, and file management
  • Role-based file resolution for multi-sample analysis
  • CRUD and pagination APIs for large-scale data spaces

Collaboration And Intelligence

  • WebSocket and SSE realtime signaling
  • LLM bridge for assistant-style analysis workflows
  • JWT and API key based access boundaries

System View

flowchart LR
    U[Researchers and Engineers] --> A[API and Realtime Layer]
    A --> B[Workflow and Data Services]
    B --> C[Container Lifecycle Control]
    C --> D[Docker or Kubernetes Runtime]
    B --> E[Project Data Persistence]
    C --> F[Outbox and Worker Execution]
    F --> C

Why Teams Choose gobrave

  • One executable deployment model
  • Explicit operational semantics
  • Strong project-scoped traceability
  • Practical path from deterministic pipelines to dynamic orchestration

Quick Start

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git clone https://github.com/gobravedev/gobrave.git
cd gobrave
cp config.example.yml config.yml
go build -o gobrave ./cmd/server
./gobrave

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